Enhanced Eigen-Audioframes for Audiovisual Scene Change Detection

Kyperountas, Marios; Kotropoulos, Constantine; Pitas, Ioannis

doi:10.1109/tmm.2007.893337

Cited by 17 publications

(7 citation statements)

References 25 publications

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“…This may be particularly useful in some real life applications such as movie/TV data indexing/retrieval, annotating media data through audio labels, and media content understanding. Figure 4 further compares the performance of HMM with the sliding window method [7] in automatically labeling audio events, from which we can see that the HMM model achieves an averagely higher accuracy than the sliding window method. …”

Section: Resultsmentioning

confidence: 99%

“…For example, Chu et al [6] perform an empirical feature analysis for audio environment characterization and propose to use the matching pursuit (MP) algorithm to obtain effective timefrequency features. Kyperountas et al [7] create an enhanced set of eigen-audioframes that is related to an audio signal subspace to discover audio background changes. Unfortunately, how to bridge the semantic gap that separates low-level auditory features and high-level auditory contents is still difficult even by combining more heterogeneous features.…”

Section: Introductionmentioning

confidence: 99%

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Audiotory Movie Summarization by Detecting Scene Changes and Sound Events

Lü

Ying-Chieh

Wang

2014

2014 22nd International Conference on Pattern Recognition

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A novel movie audio summarization framework is presented, which consists of three processing levels, namely, lowlevel audio feature extraction, mid-level audio event detection, and high-level auditory movie summarization. We first detect auditory changes in the eigen-audiospace to segment movie audio streams, then exploit a scoring algorithm to refine the segments. Audio events from movies are identified in a hierarchical manner from background detection, foreground event separation to key audio event identification, thus generating the final audio summaries from each movie. The experiments on different auditory categories from movies and TVs demonstrate the effectiveness of the propose approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Audiotory Movie Summarization by Detecting Scene Changes and Sound Events

Lü

Ying-Chieh

Wang

2014

2014 22nd International Conference on Pattern Recognition

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“…References [8][9][10] combined audio and video information as "audiovisual feature" to jointly solve the scene change detection problem, improved the detection accuracy in some degrees but also brought additional computations.…”

Section: Related Workmentioning

confidence: 99%

A Compressive Sampling Approach to Scene Change Detection

Zhang¹,

Wang

Jin³

et al. 2015

2015 First International Conference on Computational Intelligence Theory, Systems and Applications (CCITSA)

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As an initial step of intelligent video processing, the efficiency of scene change detection algorithm mostly impacts the whole processing progress. Increasing number of researchers has absorbed in improving the algorithm's accuracy or reducing the computational complexity through various approaches. This paper proposes a new approach to scene change detection which explores the high compression efficiency of compressive sampling theory, and only uses 20 compressive samplers each frame to achieve a high scene change detection accuracy. The main contributions of this paper are as follows: we first introduce the compressive sampling theory into video scene change detection technique, then found the best combination of sampler number and the threshold so as to achieve the highest accuracy, and importantly the computational complexity of the proposed algorithm keeps at a low level. The experimental results demonstrate our algorithm's efficiency: 0.93 of precision rate, 1.0 of recall rate and 0.96 of F1 score on average. Meanwhile, comparing with the algorithm with the similar computation complexity, the proposed algorithm is able to improve the detection accuracy by approximate 20%; comparing with the algorithm with the similar detection accuracy, the proposed algorithm can reduce approximate about 40% of computational time.

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“…On a news programme, we assume that the whole presentation of a piece of news is a unique scene, once all related shots talk about the news' subject. The definition of the beginning and ending timestamps of each scene is done manually, because this process is out of scope in this work; however, automatic algorithms have been reported on literature [21,23]. Following this process, we generate a set of SubRip files (SRT), using the open-source closed-caption tool CCExtractor 7 , which converts binary closed-caption captured with Microsoft GraphEdit 8 from analog sources into subtitle text.…”

Section: Indexing and Enrichment: Backgroundmentioning

confidence: 99%

An enhanced content selection mechanism for personalization of video news programmes

2010

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In this paper, we propose a content selection framework that improves the users' experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media's constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique's limitations are minimized by other techniques' strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

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Enhanced Eigen-Audioframes for Audiovisual Scene Change Detection

Cited by 17 publications

References 25 publications

Audiotory Movie Summarization by Detecting Scene Changes and Sound Events

Audiotory Movie Summarization by Detecting Scene Changes and Sound Events

A Compressive Sampling Approach to Scene Change Detection

An enhanced content selection mechanism for personalization of video news programmes

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